Question Answering Domain Adaptation

Question answering (QA) domain adaptation focuses on improving the ability of QA models to generalize to new, unseen domains. Current research emphasizes techniques like self-supervised learning, adversarial training, and data augmentation strategies (including synthetic data generation and hidden space augmentation) to bridge the performance gap between source and target domains. These methods aim to improve model robustness and reduce reliance on large, labeled target datasets, ultimately leading to more reliable and adaptable QA systems across diverse applications. The impact of this research lies in creating more versatile and practical QA systems for real-world scenarios where data scarcity or domain shifts are common.